1 peter sloot: computational science, university of amsterdam. interactive biomedical problem...

1Peter Sloot: Computational Science, University of Amsterdam.

Interactive Biomedical Problem Solving on the Grid:

Peter Sloot

sloot@science.uva.nl

Computational Science

http://www.science.uva.nl/research/scs

University of Amsterdam, The Netherlands

2Peter Sloot: Computational Science, University of Amsterdam.

3Peter Sloot: Computational Science, University of Amsterdam.

A prototypical killer application

The Grid is not about Science… it is about Engineering…

The science is in the application… -lots on High Energy Physics etc… but…

-let’s forget the ‘Rocket Science’ for a while

4Peter Sloot: Computational Science, University of Amsterdam.

Changing the Paradigm

In Vivo

In Vitro

In Silico

5Peter Sloot: Computational Science, University of Amsterdam.

Changing the Paradigm

In Vivo

In Vitro

In Silico

6Peter Sloot: Computational Science, University of Amsterdam.

Changing the Paradigm

In Vivo

In Vitro

In Silico

7Peter Sloot: Computational Science, University of Amsterdam.

From Molecule to Man…

DNA Proteins Cellular Pharma-ceutical Treatment

Genomics Proteomics Immunology Medical

Mutations ProteaseReverse Transcriptase

CD-4 Experssion# RNA particles

Vivo-Vitro- ExperimentationSilico-

Molecule

Time

Space10-14 sec

10-10 m

Years

10-1 m

Man

8Peter Sloot: Computational Science, University of Amsterdam.

•First Principle Modeling

•Genetic Regulatory Networks

•Metabolic Networks

•Immunological Networks

•… Silicon Cell

•Hierarchical data Modeling

•G-P-M & Patient Dbases

AnalyticMolecular DynamicsMonte CarloMesoscopic

AI – GA’s, NN’s, Fuzzy L.

From Molecule to Man…Cont

9Peter Sloot: Computational Science, University of Amsterdam.

From Molecule to Man…PSE/G

• Mesoscopic Simulation High Performance Computing • Parameter Space Exploration High Throughput Computing

• Data Disclosure Dbase Federation and Integration

• Data Fusion Hierarchical Parameter Transfer

• Access Visualization/VR && Roaming &&PDA

10Peter Sloot: Computational Science, University of Amsterdam.

Two Projects

AbdominalAorticAneurysm

HIV Expert System

Computational Science - ICCS 2003, Melbourne, Australia and St. Petersburg, Russia, Proceedings Part I, in series Lecture Notes in Computer Science, vol. 2657, pp. 125-135. Springer Verlag, June 2003. ISBN 3-540-40194-6.

5th International Conference on Cellular Automata for Research and Industry, ACRI 2002, Geneva, Switzerland, October 9-11, 2002. Proceedings, in series Lecture Notes in Computer Science, vol. 2493, pp. 282-293. October 2002.

11Peter Sloot: Computational Science, University of Amsterdam.

Ariadne’s red rope

Motivation Experimentalsetup

ArchitectureSimulationVisualizationInteraction

Status: Some ‘hot’ results

12Peter Sloot: Computational Science, University of Amsterdam.

Ariadne’s red rope

Motivation Experimentalsetup

ArchitectureSimulationVisualizationInteraction

Status: Some ‘hot’ results

13Peter Sloot: Computational Science, University of Amsterdam.

Current Situation

Observation

Diagnosis & Planning

Treatment

Nature March 2002

14Peter Sloot: Computational Science, University of Amsterdam.

Salami…

15Peter Sloot: Computational Science, University of Amsterdam.

Example: Proof is in the pudding...

– Diagnostic Findings

• Occluded right iliac artery

• 75% stenosis in left iliac artery

• Occluded left SFA

• Diffuse disease in right SFA

Computer Assisted Radiology and Surgery (Excerpta Medica, International Congress Series 1230), pp. 938-944. Elsevier Science B.V., Berlin, Germany, June 2001.

16Peter Sloot: Computational Science, University of Amsterdam.

Segmentation Through Wave Propagation

17Peter Sloot: Computational Science, University of Amsterdam.

Methods - MR Imaging

MR Scan of Abdomen MR Scan of Legs

18Peter Sloot: Computational Science, University of Amsterdam.

Methods - Geometric Models

19Peter Sloot: Computational Science, University of Amsterdam.

Alternate Treatments

Angio w/ Fem-Fem &

Fem-Pop

AFB w/ E-S Prox.

Anast.

Angio w/Fem-Fem

AFB w/ E-E Prox.

Anast.

Preop

Courtesy Prof. C. Taylor

20Peter Sloot: Computational Science, University of Amsterdam.

Ariadne’s red rope

Motivation Experimentalsetup

ArchitectureSimulationVisualizationInteraction

Status: Some ‘hot’ results

21Peter Sloot: Computational Science, University of Amsterdam.

Experimental set-up

MRI, PET Monolith,Cluster

Cave, Wall,PC,PDA

Advanced Infrastructures for Future Healthcare, pp. 275-282. IOS Press, 2000.

22Peter Sloot: Computational Science, University of Amsterdam.

Design Considerations

– FACTS: New Scanners 1024 x 1024: 128 slices of 2 byte depth == 256 MByte, 10 images per systole == 1 per second

– High Quality presentation– High Frame rate– Intuitive interaction– Real-time response– Interactive Algorithms– High performance computing and networking...

23Peter Sloot: Computational Science, University of Amsterdam.

Provoking a bit…

Progress in natural sciences comes from taking things apart ...

Progress in computer science comes from bringing things together...

24Peter Sloot: Computational Science, University of Amsterdam.

Ariadne’s red rope

Motivation Experimentalsetup

ArchitectureSimulationVisualizationInteraction

Status: Some ‘hot’ results

25Peter Sloot: Computational Science, University of Amsterdam.

PSE/G Architecture

PDAAccess

&SMS

Service

‘Dynamite/G to support

Checkpointing Parallel HPC

Programs

Concurrency and Computation: Practice and Experience, ((Special Issue on Grid Computing Environments)) vol. 14, pp. 1313-1335. John Wiley and Sons, 2002.

Lecture Notes in Computer Science, vol. 1971, pp. 203-213. Springer-Verlag, December 2000.

26Peter Sloot: Computational Science, University of Amsterdam.

Building the workflow

Contract:

Capability specification

Experiment specification.

Components in Grids wide

Component Broker

Components Candidates

Design a story for components.

Execute the story.

1.

2.

3.

Software bus

Call for agents.

Proceedings of the Communication Networks and Distributed Systems Modeling and Simulation Conference (CNDS 2002), pp. 3-10. January 2002.

27Peter Sloot: Computational Science, University of Amsterdam.

Actors setting the scene

Start[,]

InitSim[Ds,]

doStep[,]

ExportData[,Dr]

Stop[,]

(succeed,CdU(Ds)

E_Act(init))

(succeed,_,E_Act(doStep))

(succeed,E_Act(stop))

(succeed,_

E_Act(exportData))

(succeed,_E_Act(doStep))

(succeed, _, do(doStep))

(succeed,E_Act(stop))

Actor Conductor

Story

Software bus: RTI of the HLA/G

Communication Agents:Interfacing to software bus.

Module Agents:Application specific activities = Story + my capability.

Story:Application specific specification.

Actor:Doing real activities.

Implementation of actions.

28Peter Sloot: Computational Science, University of Amsterdam.

Story = {Scenarios}

Start Story

End Story

Scenario 2

Scenario 1

([a=1

], [])

([a>3

], [])

([a<

=3]

, [a

+=

1])

([a<

=3]

, [a

+=

1])

(simulation, startScenario)

(p1,1, [], [],[])

(p3,0, [],[], [])(p2,0, [sa=1],[sa<100], [sa+=1])

(visualization, visualisedata)(simulation, compute)

(simulation, endScenario)

(p4,0, [ ], []) (p5,0, [],[], [])

(p6,0,[], [], [])Story: scenario transition graph.

Nodes: {scenarios};

Transitions: {([Ee], [Eq])};

[Ee]: expression when entering;

[Eq]: expressions when leaving.

Scenario: P/T net:Places: {(name, token, [Ei], [Ee], [Eq])}

[Ei]: expressions when init the P/T net;[Ee]: expressions when enter a place;[Eq]: expressions when leave a place.

Transitions: {(role, action)}

29Peter Sloot: Computational Science, University of Amsterdam.

Why HLA:

Simulation modules aggregation and reuse; Large and mature user base; legacy

But:

HLA requires explicit description of data and event objects that will be exchanged before the actual federation starts execution Static bootstrapping process required to enable RTI system communication

Our approach:

GT3 index service and data transfer infrastructure evaluated to assess capabilities and limitations with HLA integration

Performance of GT3 GIS infrastructure for querying and modifying RTIexec endpoint information and RTI bootstrap

Current GT3 GIS performance demonstrates the feasibility of the GT3/HLA based GIS for HLA runtime information query, RTI dynamic modification of HLA Federates, and bootstrapping

Added features to HLA like security, extensibility, scalability, and decentralized maintenance.

Implementation: RTI 1.3 NG V5, Amzi Prolog

Grid Service query performance, secured bindings

Migration approach from HLA to GS

Grid Services and HLA

Grid Services for HLA-based Distributed Simulation Frameworks, in First European Across Grids Conference, Santiago de Compostela, Spain, Springer-Verlag, Heidelberg, February 2003.

30Peter Sloot: Computational Science, University of Amsterdam.

Ariadne’s red rope

Motivation Experimentalsetup

ArchitectureSimulationVisualizationInteraction

Status: Some ‘hot’ results

31Peter Sloot: Computational Science, University of Amsterdam.

Flow through complex geometry

– After determining the vascular structure simulate the blood-flow and pressure drop…

– Conventional CFD methods might fail:• Complex geometry• Numerical instability wrt interaction• Inefficient shear-stress calculation

32Peter Sloot: Computational Science, University of Amsterdam.

Solution to interactive flow simulation

– Use Cellular Automata as a mesoscopic model system:• Simple local interaction• Support for real physics and heuristics• Computational efficient

33Peter Sloot: Computational Science, University of Amsterdam.

Mesoscopic Fluid Model

– Fluid model with Cellular Automata rules

– Collision: particles reshuffle velocities

– Imposed Constraints• Conservation of mass• Conservation of momentum• Isotropy

Details...

34Peter Sloot: Computational Science, University of Amsterdam.

...Equivalence with NS– For lattice with enough symmetry: equivalent to the continuous incompressible Navier-Stokes equations:

uuu

u

2 1

0

Pt

u

Implicit parallel and complex geometry support.

35Peter Sloot: Computational Science, University of Amsterdam.

Efficient Calculation of Shear-Stress

AND the momentum stress tensor that is linearly related to the shear stresses

i

if i

iif cu

i

iiif ccΠ

x

u

x

u

~

Perpendicular momentum transfer:

From LBE scheme:

36Peter Sloot: Computational Science, University of Amsterdam.

Visualization Courtesy J. Steinman

International Journal of Modern Physics B, vol. 17, nr 1&2 pp. 95-98. World Scientific Publishing Company, January 2003.

International Journal of Modern Physics C, vol. 13, nr 8 pp. 1119-1134. October 2002.

37Peter Sloot: Computational Science, University of Amsterdam.

T.S. Elliot

‘How much wisdom has been lost in knowledge and how much knowledge has been lost in

information...’

How much Information has been lost in Data!!

Fourth IEEE ACMI'02 International Conference on Multimodal Interfaces, Pittsburgh, Pennsylvania, 14-16 October 2002, pp. 313-318. IEEE Computer Society, Los Alamitos, California, USA, 2002.

38Peter Sloot: Computational Science, University of Amsterdam.

Immersive Environments

39Peter Sloot: Computational Science, University of Amsterdam.

3D Information and Interaction

40Peter Sloot: Computational Science, University of Amsterdam.

VR-Interaction

41Peter Sloot: Computational Science, University of Amsterdam.

VR Portal

42Peter Sloot: Computational Science, University of Amsterdam.

Ariadne’s red rope

Motivation Experimentalsetup

ArchitectureSimulationVisualizationInteraction

Status: Some ‘hot’ results

43Peter Sloot: Computational Science, University of Amsterdam.

DAS2 SEs Storage Elements (SE) in UvA, NIKHEF, and Leiden EDG 2.0 release candidate with VDT-1.1.8-6, installed and configured manually instead of LCFG, because is

a shared system No R-GMA running, using the MDS interface on port 2135 Distributed ASCI Supercomputer 2 (DAS-2) Myrinet multi-Gigabit wide-area distributed computer of 200 Dual Pentium-III nodes Fast Ethernet used as OS network Each node contains:

Two 1-Ghz Pentium-IIIs At least 1 GB RAM At least 20 GByte local IDE disk A Myrinet and Fast Ethernet interface cards Linux 2.4.7-10

CrossGrid CEs Currently EDG 1.2.2 and 1.2.3 deployed in the production testbed EDG 1.4.3 being tested at several validation sites Production testbed resources:

15 Computing Elements 69 Worker Nodes 115 CPUs 2.7TB Storage Capacity

MD Local repository for remote navigation, data transfer and D-VRE initialization Requirements:

Web Browser Java Plugin 1.4 or newer with JAR cache disabled Firewalls open for 8080 port Pool 13000-17000 and port 2811 open in both directions between local workstation and remote SE

(se.crossgrid.man.poznan.pl) Valid x509 certificate credentials

The Specs

44Peter Sloot: Computational Science, University of Amsterdam.

Shear stress, velocities,

masses, etc.

ce (CrossGrid)

se2 (D-VRE machine)

MD login and Grid certificates submission

Bypass creation LB mesh generation

Job submission Job monitoring

MR image Segmentation(soon a GS!)

se1 (e.g., Leiden)

Patient at MRI scanner

MR image

Virtual Node Creation

The Scenario

Simulated flow

ce (CrossGrid)

I have no GVK images,

ask Elena

Proceedings of the First European HealthGrid Conference, January, 16th-17th, 2003, pp. 57 - 66.

45Peter Sloot: Computational Science, University of Amsterdam.

Recorded Session

September 25th 2003

46Peter Sloot: Computational Science, University of Amsterdam.

A peek in the kitchen…

47Peter Sloot: Computational Science, University of Amsterdam.

48Peter Sloot: Computational Science, University of Amsterdam.

Wrapping up– Agent based iPSE (Simulation, Interaction and Visualization)– Vascular Reconstruction– Dynamic task Migration support for HPC on the Grid– Migrating Desktop X# integrated with VRE

– Working on:• Human Machine Interaction (trial in 3 Hospitals)• Stent Placement• Alternative VR: DesktopVR • OGSA and HLA-Grid

49Peter Sloot: Computational Science, University of Amsterdam.

AcknowledgementsStanford:

Charley Taylor, PhD.

Christopher K. Zarins, PhD. M.D.

UvA:

Denis Shamonin

Roman Shulakov

Alfredo Tirado Ramos

Robert Belleman, PhD

Alfons Hoekstra, PhD

Dick van Albada, PhD

Elena Zudilova, PhD

RUL/AZL:

H. Reiber, PhD.

Bloem, PhD, M.D.

U. Wisconsin

M. Livney, PhD

SARA:

A. de Koning, PhD.

Krakow

Marian Bubak, PhD

Katarzyna Zajac

X#X#

50Peter Sloot: Computational Science, University of Amsterdam.

http://www.science.uva.nl/research/scs